A Park's vector approach using process monitoring statistics of principal component analysis for machine fault detection

2016 ◽  
Author(s):  
Armughan Hameed ◽  
Sufi Tabassum Gul ◽  
Abdul Qayyum Khan
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Process Monitoring ◽  
Principal Component ◽  
Component Analysis ◽  
Machine Fault ◽  
Vector Approach

scholarly journals Online monitoring scheme using principal component analysis through Kullback-Leibler divergence analysis technique for fault detection

2019 ◽  
Vol 42 (6) ◽  
pp. 1225-1238 ◽  
Author(s):  
Wahiba Bounoua ◽  
Amina B Benkara ◽  
Abdelmalek Kouadri ◽  
Azzeddine Bakdi
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Process Monitoring ◽  
Control Charts ◽  
Principal Component ◽  
Component Analysis ◽  
Statistical Process ◽  
Multivariate Gaussian Distribution ◽  
Tennessee Eastman Process ◽  
Leibler Divergence

Principal component analysis (PCA) is a common tool in the literature and widely used for process monitoring and fault detection. Traditional PCA is associated with the two well-known control charts, the Hotelling’s T2 and the squared prediction error (SPE), as monitoring statistics. This paper develops the use of new measures based on a distribution dissimilarity technique named Kullback-Leibler divergence (KLD) through PCA by measuring the difference between online estimated and offline reference density functions. For processes with PCA scores following a multivariate Gaussian distribution, KLD is computed on both principal and residual subspaces defined by PCA in a moving window to extract the local disparity information. The potentials of the proposed algorithm are afterwards demonstrated through an application on two well-known processes in chemical industries; the Tennessee Eastman process as a reference benchmark and three tank system as an experimental validation. The monitoring performance was compared to recent results from other multivariate statistical process monitoring (MSPM) techniques. The proposed method showed superior robustness and effectiveness recording the lowest average missed detection rate and false alarm rates in process fault detection.

scholarly journals A distributed expectation maximization-principal component analysis monitoring scheme for the large-scale industrial process with incomplete information

2019 ◽  
Vol 15 (11) ◽  
pp. 155014771988549
Author(s):  
Xuanyue Wang ◽  
Xu Yang ◽  
Jian Huang ◽  
Xianzhong Chen
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Process Monitoring ◽  
Expectation Maximization ◽  
Large Scale ◽  
Principal Component ◽  
Component Analysis ◽  
Missing Information ◽  
Tennessee Eastman Process ◽  
Analysis Scheme

Large-scale process monitoring has become a challenging issue due to the integration of sub-systems or subprocesses, leading to numerous variables with complex relationship and potential missing information in modern industrial processes. To avoid this, a distributed expectation maximization-principal component analysis scheme is proposed in this paper, where the process variables are first divided into several sub-blocks using two-layer process decomposition method, based on knowledge and generalized Dice’s coefficient. Then, the missing information of variables is estimated by expectation maximization algorithm in the principal component analysis framework, then the expectation maximization-principal component analysis method is applied for fault detection to each sub-block. Finally, the process monitoring and fault detection results are fused by Bayesian inference technique. Case studies on the Tennessee Eastman process is applied to show the effectiveness and performance of our proposed approach.

scholarly journals Monitoring a Reverse Osmosis Process with Kernel Principal Component Analysis: A Preliminary Approach

2021 ◽  
Vol 11 (14) ◽  
pp. 6370
Author(s):  
Elena Quatrini ◽  
Francesco Costantino ◽  
David Mba ◽  
Xiaochuan Li ◽  
Tat-Hean Gan
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Water Purification ◽  
Principal Component ◽  
Temporal Correlation ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Original Dataset ◽  
Monitoring Process ◽  
Machine Health

The water purification process is becoming increasingly important to ensure the continuity and quality of subsequent production processes, and it is particularly relevant in pharmaceutical contexts. However, in this context, the difficulties arising during the monitoring process are manifold. On the one hand, the monitoring process reveals various discontinuities due to different characteristics of the input water. On the other hand, the monitoring process is discontinuous and random itself, thus not guaranteeing continuity of the parameters and hindering a straightforward analysis. Consequently, further research on water purification processes is paramount to identify the most suitable techniques able to guarantee good performance. Against this background, this paper proposes an application of kernel principal component analysis for fault detection in a process with the above-mentioned characteristics. Based on the temporal variability of the process, the paper suggests the use of past and future matrices as input for fault detection as an alternative to the original dataset. In this manner, the temporal correlation between process parameters and machine health is accounted for. The proposed approach confirms the possibility of obtaining very good monitoring results in the analyzed context.

Online reduced kernel principal component analysis for process monitoring

2018 ◽  
Vol 61 ◽  
pp. 1-11 ◽  
Author(s):  
Radhia Fezai ◽  
Majdi Mansouri ◽  
Okba Taouali ◽  
Mohamed Faouzi Harkat ◽  
Nasreddine Bouguila
Keyword(s):  
Principal Component Analysis ◽  
Process Monitoring ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis
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